import json
import sys


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Tuple:




  '''simple docstring'''

  with open(a__    ,     encoding="utf-8" ) as f:
    _UpperCamelCase           =    json.load(a__ )

  _UpperCamelCase           =    ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

  for benchmark_name in sorted(a__ ):
    _UpperCamelCase           =    results[benchmark_name]

    _UpperCamelCase           =    benchmark_name.split("/" )[-1]
    output_md.append(f'### Benchmark: {benchmark_file_name}' )

    _UpperCamelCase           =    "| metric |"
    _UpperCamelCase           =    "|--------|"
    _UpperCamelCase           =    "| new / old (diff) |"
    for metric_name in sorted(a__ ):
      _UpperCamelCase           =    benchmark_res[metric_name]
      _UpperCamelCase           =    metric_vals["new"]
      _UpperCamelCase           =    metric_vals.get("old"    ,     a__ )
      _UpperCamelCase           =    metric_vals.get("diff"    ,     a__ )

      _UpperCamelCase           =    f' {new_val:f}' if isinstance(a__    ,     (int, float) ) else "None"

      if old_val is not None:
        val_str += f' / {old_val:f}' if isinstance(a__    ,     (int, float) ) else "None"
      if dif_val is not None:
        val_str += f' ({dif_val:f})' if isinstance(a__    ,     (int, float) ) else "None"

      title += " " + metric_name + " |"
      lines += "---|"
      value += val_str + " |"

    output_md += [title, lines, value, " "]

  output_md.append("</details>" )

  with open(a__    ,     "w"    ,     encoding="utf-8" ) as f:
    f.writelines("\n".join(a__ ) )


if __name__ == "__main__":
   lowerCamelCase__  =					sys.argv[1]
   lowerCamelCase__  =					sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    3
   _UpperCamelCase           =    250

   _UpperCamelCase           =    ids_tensor((batch_size, length)							, lowercase_)
   _UpperCamelCase           =    torch.ones((batch_size, length)							, device=lowercase_							, dtype=torch.float) / length
   return input_ids, scores







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10),
           MaxTimeCriteria(max_time=0.1),
       ])

   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxLengthCriteria(max_length=10)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxNewTokensCriteria(start_length=5							, max_new_tokens=5)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    StoppingCriteriaList([criteria])
   self.assertEqual(criteria_list.max_length							, 10)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1							, initial_timestamp=time.time() - 0.2)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 10)

   with self.assertWarns(lowercase_):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 11)

   _UpperCamelCase           =    validate_stopping_criteria(StoppingCriteriaList()							, 11)

   self.assertEqual(len(lowercase_)							, 1)




